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# Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

- Fei He
^{1, 2}, - Yuanning Liu
^{1, 2}, - Xiaodong Zhu
^{1, 2}Email author, - Chun Huang
^{1, 2}, - Ye Han
^{1, 2}and - Hongxing Dong
^{1, 2}

**2014**:95

https://doi.org/10.1186/1687-6180-2014-95

© He et al.; licensee Springer. 2014

**Received: **2 April 2014

**Accepted: **30 May 2014

**Published: **19 June 2014

## Abstract

Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

## Keywords

## Introduction

The first iris recognition system was proposed by Daugman in 1993 and is still the state-of-the-art technique used today [1, 2]. Wildes’ iris biometric method is another important approach [3, 4]. Subsequently, a large number of algorithms have been developed to develop an iris practical system with less control [5–7]. However, the commercial iris recognition system still has problems, such as intra-class variations (e.g., iris texture affected by ageing), inter-class similarities (leads to false acceptance), and noise in data (e.g., illumination effect to iris image pixels) [8].

Feature extraction is a crucial stage for addressing these problems [9]. Two options exist to improve iris recognition performance. One option is to find effective and fast iris representation for various acquisition conditions. Daugman presented iris texture using Gabor phase-based code that is invariant under the non-affine elastic distortion [2]. Huang et al. proposed a new rotation invariant iris feature based on the non-separable wavelet and the Markov random model [10]. These methods analyzed iris texture properties in the frequency domain based on Fourier and wavelet transforms. However, these methods have the disadvantage of fixed transform kernels and cannot pertinently match the changing nature of different iris datasets. Some researchers select an optimal features subset based on filtered coefficients to enhance their distinctive information [11], but what these researchers adopted for filtering is still a predefined log-Gabor wavelet. Chang et al. discovered an unconventional approach that applied an improved empirical mode decomposition (EMD) method. The EMD method is a multi-resolution decomposition technique without any predetermined filter or wavelet function to the iris pattern extraction [12]. Moreover, several approaches exist that directly scan geometric descriptors at the iris images. Mehrotra et al. selected iris local features using the scale invariant feature transform (SIFT) [13]. However, several key problems, such as illumination variations, environmental conditions, and device variations, cannot be fully addressed using a single form of iris feature.

The second option is to extract multiple iris features and then combine them to compensate for the weakness of a single feature in particular situations. Raja Sekar et al. presented a fusion method of statistical and co-occurrence features that were extracted from the curvelet and ridgelet transformed images. The Manhattan distance and the multiclass classifier with a logistic function were used to generate the final classification result [14]. Tan et al. utilized ordinal measures, color analysis, texture representation, and semantic information as iris features as well as the weighted sum rule to generate the fused score for classification [15]. Gong et al. selected three wavelength bands to represent an iris and then integrated them using agglomerative clustering based on a two-dimensional principal component analysis [16]. The fusion of multiple features is regarded as a positive step towards the development of extremely ambitious types of iris recognition [17]. However, the fusion of multiple features should overcome the challenge of the heterogeneous manifestation of various features.

The algorithm benefited from the immense global searching ability of PSO and can obtain an optimal single Gaborfilter. However, the whole Gabor filter set may not be effective. Possible overlaps may exist among the Gabor bands and may include redundant information for the Gabor features. To address this problem, we propose a Gabor filter optimization algorithm based on PSO and its binary version Boolean PSO (BPSO). In our work, the lower dimensional parameters of the Gabor filter set with an orthogonal kernel constraint condition are analyzed. For the real values and integer values in Gabor set parameters, PSO and BPSO rules are utilized to obtain the optimal Gabor bands for each involved dataset. Our method can adaptively determine the amount of Gabor filters, bandwidth, and covered frequency bandwidth to generate the orthogonal Gabor filters in place of manual modulation. The optimized Gabor filters may cater to the diverse frequency coverage of various iris datasets and are related to the capture device, acquisition condition, and individual physiology. Moreover, local features can offer a closer analysis of the uniqueness of the iris texture but are generally included in the irregular distribution of the iris image blocks such as crypts, freckles, coronas, stripes, and furrows [30]. Therefore, we divided the Gabor response magnitude and phase to generate local feature vectors, where iris texture can be further preserved. Compared with the traditional localized mode of iris image division, our localized method occurred after convolution and may avoid the blocking effect in the process of image division.

To fuse multiple iris features, we extract two different types of Gabor features to describe the iris texture from the energy spectrum and frequency domain and then combine them using a new non-linear fusion strategy. The Gabor response magnitude is the model of orientation for the selective neuron in the primary visual cortex [31] and is related to the local energy spectrum, while the Gabor phase can capture the information from the wavelet’s zero crossing [2]. An advantage of our multiple feature extraction is lower computational complexity because both features can be calculated by only one Gabor transformation. Furthermore, in the process of Gabor transformation, DC-free Gabor kernel is adopted because of its invariance property to the ambient illumination [32], while Gabor phase features of irises are assigned regardless of how low the image contrast, illumination, and camera gain [33]. Thereby, our proposed system involved extensive Gabor energy features that may perform more robustness and reliability when illumination variation exists. For the combination of the two kinds of Gabor features, we prefer a more flexible score level fusion as opposed to a feature level fusion, which needs to address the heterogeneity of various features, and a decision level fusion, which involves less information [34]. The match score is a real value measure of the similarity between the input and template biometric feature vectors. In score level fusion, all real value scores from multiple features will be combined into a real value to arrive at a final recognition decision. A robust and effective score fusion method based on support vector regression (SVR) is proposed in this paper. This method may fuse matching scores from different features using a non-linear and high-dimensional regression function, which will better fit the non-correlations among matching scores from multiple features.

The remainder of this paper is organized as follows: Section ‘Gabor filters’ introduces the generation process of two types of local Gabor features and their matching scores. Section ‘Gabor filter optimization by PSO and BPSO’ illustrates the Gabor filter optimization by PSO and BPSO. Section ‘Score fusion scheme based on SVR’ describes multiple Gabor feature fusion schemes based on SVR. Section ‘Experimental results’ presents the experimental setup and results. Finally, Section ‘Conclusion’ concludes the paper.

## Gabor filters

### Gabor function

*μ*and

*ν*determine the objective orientation and scale, respectively. The center of the receptive field is

*z*= (

*x*,

*y*). The norm operator is ∥∙∥. The standard derivation of the Gaussian envelope is

*σ*, which determines the ratio of the Gaussian width to the wavelength. We adopt the DC-free Gabor kernel that offers an invariance property to the ambient illumination change in the iris image acquisition [37]. The wave vector is defined as follows:

where *k*_{
ν
}and *ϕ*_{
μ
}are the frequency and orientation of targeted texture.

### Multi-channel Gabor filters

*k*

_{ ν }in Equation 4 can be computed as follows:

*K*

_{max}is the maximum frequency that defines the covered frequency band,

*f*is the frequency scaling factor, and

*M*defines the number of all extracted scales. An important wavelet property that provides the orthogonal basis to Gabor functions is inherited by Equation 4 [38]. The standard derivation

*σ*is

*σ*=

*K*

_{max}/2(2

^{ M }-1). The selection of targeted angles is

*ϕ*

_{ μ }calculated as follows:

In that, *N* is the number of targeted orientations. Therefore, a quadruple of Gabor parameters {*K*_{max},*f*,*M*,*N*} determines a set of Gabor filters.

### Two types of localized Gabor features

In this paper, we extract two different types of local Gabor features based on the Gabor responses and their division. To generate local Gabor features, using Gabor response division instead of image division can eliminate the blocking effect in the process of convolution. The blocking effect of image division will lead some staircase noises into Gabor transformation [40]. If several iris characteristics exist in a localized block, they will be degraded such that the block boundary looks like the edge. Further, the accuracy and reliability of the Gabor features will be badly hurt.

One type of localized Gabor feature in our work is generated by dividing each Gabor response magnitude into *r* × *c* size blocks. The statistical means of all blocks constitute a local energy Gabor feature because the Gabor response magnitude is related to the local energy spectrum. The matching score may be calculated using the Euclidean distance (ED). To eliminate the effects of dimension, the *L*_{2} norm of each iris feature may be designated in the ED computation.

The second type of localized Gabor feature, called the local Gabor phase feature, is generated by dividing each Gabor response phase into *r* × *c* size blocks. Next, each block is encoded in accordance with the Daugman rule [1]. Hamming distance (HD) is used to compute across a population of unrelated phase codes bit by bit.

## Gabor filter optimization by PSO and BPSO

### The concepts of PSO and BPSO

*P*

_{ i }= (

*p*

_{i,1},

*p*

_{i,2},⋯,

*p*

_{i,n}) is initialized for an optimal solution by updating their values with its own velocity

*V*

_{ i }= (

*v*

_{i,1},

*v*

_{1,2},⋯,

*v*

_{i,n}). In recursions, all particles and their velocities are replaced by the best previous position of the current particle ${P}_{{\text{best}}^{i}}$ and the best previous position of all particles

*G*

_{best}as follows:

where *p*_{i,j}is the *i* th and *j* th dimensional particle. The inertial weight is *w* and is generated in the range [0,1] [43]. The velocity *v*_{i,j} is restricted to the range [ -*V*_{max},*V*_{max}].

A fitness function is defined to evaluate the position of the particles. After a limited number of recursions, the particle that satisfies the global best fitness is chosen as the optimal result.

*M*and

*N*. Kennedy and Eberhart provided the concept and principle of PSO in a discrete domain, named BPSO [44]. In BPSO, the definition of velocity is developed into a probability in which a certain bit position will receive 1 value [45]. Equations 8 and 9 are rewritten as follows:

where ⊕ means the ‘xor’ operator, and ⊗ and + are the ‘and’ and ‘or’ operators, respectively. c1 and c2 control the probability that every bit of $({P}_{i,j}^{\text{best}}\oplus {p}_{i,j}(t\left)\right)$ and (*G*_{best} ⊕ *p*_{i,j}(*t*)) will take 1 value. The constraint of maximum velocity still exists in BPSO but limits the number of 1-value bits in velocity.

### Gabor filter optimization by PSO and BPSO

*μ*and

*σ*

^{2}denote the mean and variance of inter-class and intra-class distance, respectively. The fitness of an optimal objective therefore can be defined as follows:

*d*

_{energys}and

*d*

_{phase}are the DI values, respectively, produced by the Gabor energy and phase features according to the Gabor filters represented in each particle. The larger the fitness points, the better the position in the problem space. In this paper, optimized Gabor filters should achieve a trade-off of performance between the energy feature and the phase feature, so that the scale factor

*α*is set 0.5. In each recursion, according to their fitness values, we can determine the best previous position of each particle ${P}_{{\text{best}}^{i}}$ and the best previous position of all particles

*G*

_{best}to update the velocity and the position of all particles. During the execution of iterations, two real-number parameters

*K*

_{max}and

*f*will follow the PSO rules, while the discrete parameters

*M*and

*N*follow the BPSO rules. At the end of the iterations, all we have to do for determining the best particle is just to observe their fitness values of all particles and figure out the particle with the largest fitness value. The best fitness suggests that its corresponding Gabor filters best fit the MIB of the involved iris dataset, as they generate local Gabor energy and phase features with greatest distinctive ability. The flowchart of the Gabor filter optimization algorithm is shown in Figure 4.

## Score fusion scheme based on SVR

*f*(

*x*) in SVR can be denoted as follows:

where *k* is the number of training data. The Lagrangian multipliers ${\beta}_{i}^{\ast},{\beta}_{i}$ are found by solving a quadratic programming problem [49], and *b* is the bias. A kernel function *K*(*u*,*v*) performs the non-linear mapping. Any symmetric function that satisfies Mercer’s condition can be chosen as *K* (*u*,*v*). The usual kernels include dot, polynomial, radial basis function (RBF), and neuron kernels [50].

We take advantage of the SVR to fit a function *f*(*x*), which may map multiple matching scores to a fused score to make the final decision of arbitrary one-to-one identity. The ED and HD of the Gabor energy and phase features in a comparison are formed as an input vector, both of which have been normalized to the [0, 1] real-value range. This intrinsic characteristic of matching scores just might naturally avoid the question of heterogeneous input of fusion. To train a SVR model, all input data with labels from arbitrary one-to-one comparisons of enrolled irises are used to train an SVR model. The authentic comparisons will be labeled 0 as the observed value, while the imposter comparisons will be labeled 1. In the forecast mode of trained SVR, an input score vector may be mapped to a real value as its fused score. This value can be considered to integrate multiple local Gabor features to measure similarity between two irises. A lower value (close to the authentic label) obtained by output of the SVR demonstrates that the test iris and the involved enrolled iris are in the same pattern class. In light of this principle, only a reasonable threshold should be chosen to complete the classification decision.

## Experimental results

### Datasets

**Dataset description**

Number | Dataset | Class | Samples | Enrollment | Test |
---|---|---|---|---|---|

per class | per class | per class | |||

1 | CASIA-I | 50 | 7 | 4 | 3 |

2 | CASIA-V4-Interval | 50 | 7 to 10 | 5 | 2 to 5 |

3 | JLUBR-IRIS | 50 | 45 | 25 | 20 |

Every iris image in Table 2 is manually selected from accurate iris region segmentation by the Canny operator and the Hough transformation [35] to prevent interference caused by iris misalignment.

### Results

*K*

_{max}= 64,

*f*= 2,

*M*= 6,

*N*= 4) introduced in the literature [23] and the same localized block size. Their obtained correct recognition rate (CRR) of intra-class comparisons can be found in Figure 9. As the experimental results show, 19.28%, 21.73%, and 19.88% false non-matches of intra-class comparisons can be prevented by the ROI extraction when using Gabor energy features on three iris datasets, respectively. Meanwhile 19.31%, 19.87%, and 22.84% false non-matches of intra-class comparisons can be reduced when using Gabor phase features on three iris datasets, respectively. It means that all kinds of useful iris feature extraction should be performed only when the ROI region may be functioned well and the redundant eyelids and eyelashes can be excluded from the stage. The ROI extraction is thus an indispensable part in our system. The greatest improvement of CRR emerges on JLUBR-IRIS among three iris datasets, which implies that the iris images from this dataset contain more challenging disturbances including eyelids and eyelashes.

*K*

_{max}= 64,

*f*= 2,

*M*= 6,

*N*= 4) to extract the Gabor features. In this test, we divide the ROI images to generate features that represent the existing localized method [55]. Various grid search-based block sizes are used to analyze two localized ways. Figures 10,11,12,13,14,15 shows the relationships between DIs and block sizes in two localized means. From Figures 10,11,12,13,14,15, the smallest block size is not the most suitable for localized features, and if the block size is focused excessively on the minute texture, the local features will not enhance the iris texture information but will include redundant noises. Therefore, the block size of localization has to be adjusted for different batch samples. Furthermore, for Gabor energy and phase features, our proposed localized way can obtain more powerful local features and can reveal that our localized approach may conserve more texture in the process of image division and convolution. In all of the following experiments, the localized block size that obtains the best discriminative ability will be adopted.

**Optimized fitness and optimized Gabor filters on three datasets**

Dataset | DI/predefined Gabor | DI/optimized Gabor | Gabor parameters | |||||
---|---|---|---|---|---|---|---|---|

Energy | Phase | Energy | Phase |
K
| f | M | N | |

CASIA-I | 3.213 | 3.102 | 3.893 | 3. 906 | 38.199 | 0.800 | 6 | 31 |

CASIA-V4-Interval | 3.007 | 3.164 | 3.704 | 3.618 | 19.504 | 0.569 | 6 | 27 |

JLUBR-IRIS | 3.030 | 2.570 | 4.173 | 3.785 | 10.125 | 0.333 | 12 | 7 |

**The DI values from the fused scores via NU-SVR with four kernels on three datasets**

Dataset | Kernels | |||
---|---|---|---|---|

Linear | Poly | RBF | Sigmoid | |

CASIA-I | 4.178 | 2.710 | 4.422 | 0.797 |

CASIA-V4-Interval | 3.488 | 2.124 | 3.947 | 1.248 |

JLUBR-IRIS | 2.642 | 4.183 | 3.563 | 0.486 |

**Execution speeds of various operations for an iris match**

Operation | Time (msec) |
---|---|

Localized and normalized iris region | 141 |

Optimized Gabor filters filtering | 236 |

Localized energy and phase feature generation | 5.2 |

Multi-feature dissimilarity scores of two irises | 2.9 |

Gabor multi-feature fusion decision on trained SVR | 3.2 |

## Conclusion

This paper has introduced an iris recognition system with multiple local Gabor feature extractions and fusion. This system uses two types of Gabor features generated by dividing the Gabor response magnitude and phase to represent an iris. This system then trains an SVR model to fuse them at score level for identification. In the process of Gabor filtering, adopted Gabor filters are optimized by a proposed PSO and BPSO optimization method. Our system has the advantages of adaptively tuning Gabor parameters and embedding richer informative texture into features and non-linear fusion strategies. The experimental results on our self-developed JLUBR-IRIS, public CASIA-I, and CASIA-V4-Interval iris datasets indicate that our localized method can avoid blocking effects to save more information when compared with other existing localized ways. The discriminative ability of the Gabor features demonstrates that the optimized Gabor filters may match the most informative frequency band iris texture than the predefined filters. Our score fusion by an SVR model is superior to other single-feature methods in terms of DI and ROC curves. In this paper, we also compared the proposed method with other algorithms and proved its validity and superiority.

## Declarations

### Acknowledgements

In this paper, portions of the research use the CASIA-IrisV4 and CASIA I datasets collected by the Chinese Academy of Sciences Institute of Automation (CASIA).

## Authors’ Affiliations

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## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.